区块链驱动的联邦学习：BLADE-FL，保护隐私与提升安全性

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联邦学习

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本文探讨了当联邦学习（Federated Learning, FL）遇到区块链（Blockchain）时，产生的一个新兴的分布式学习范式——基于区块链的联邦学习（BC-FL）。随着用户设备计算能力的爆炸性增长以及对分享敏感原始数据隐私问题的关注日益增强，FL作为一种新兴技术应运而生。它通过在本地设备上训练模型，然后在中心服务器上聚合，实现了数据不直接共享，从而减少了隐私泄露的风险。然而，传统FL框架严重依赖单一的中央服务器，这使得其面临单点故障的问题。当中央服务器出现恶意行为时，整个系统可能会崩溃。为解决这一问题，研究者提出了一个名为BLADE-FL（Blockchain-assisted Decentralized Federated Learning）的新框架。这个框架利用区块链技术，将FL的集中架构转变为去中心化，极大地增强了系统的鲁棒性，防止了恶意客户端对学习过程的破坏。 BLADE-FL的主要贡献包括： 1. **新框架设计**：构建了一个基于区块链的FL架构，通过区块链网络取代了传统的中心服务器，实现了分布式且去中心化的学习环境。每个参与方都可以在保持数据隐私的同时，参与到模型的训练和更新过程中。 2. **安全防护**：利用区块链的加密技术和共识机制，BLADE-FL能够防止恶意节点篡改数据或执行欺诈行为，提高了整个系统的安全性。通过分布式账本，所有参与者可以验证模型的完整性和真实性，降低了数据被操纵的风险。 3. **激励机制**：文章还提出了激励机制，以鼓励参与各方积极参与到训练过程中。这可能是通过奖励机制，比如根据贡献度分配代币或者信用积分，从而激励用户诚实协作，共同维护系统的正常运行。 4. **可靠性保障**：区块链的不可篡改性确保了学习过程的透明度和可追溯性，即使有异常发生，也能追踪到问题源头，增强了系统的稳定性。总结来说，这篇论文不仅展示了如何融合区块链技术和联邦学习来构建一个更加安全、可靠和自我驱动的分布式学习环境，还提供了新的方法来应对FL中的信任问题。这为未来的隐私保护和数据共享提供了重要的理论支撑和技术路径。

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When Federated Learning Meets Blockchain: A

New Distributed Learning Paradigm

Chuan Ma, Member, IEEE, Jun Li, Senior Member, IEEE, Ming Ding, Senior Member, IEEE,

Long Shi, Member, IEEE, Taotao Wang, Member, IEEE,

Zhu Han, Fellow, IEEE, and H. Vincent Poor, Fellow, IEEE

Abstract—Motivated by the explosive computing capabilities

at end user equipments, as well as the growing privacy con-

cerns over sharing sensitive raw data, a new machine learning

paradigm, named federated learning (FL) has emerged. By

training models locally at each client and aggregating learning

models at a central server, FL has the capability to avoid sharing

data directly, thereby reducing privacy leakage. However, the

traditional FL framework heavily relies on a single central server

and may fall apart if such a server behaves maliciously. To

address this single point of failure issue, this work investigates a

blockchain assisted decentralized FL (BLADE-FL) framework,

which can well prevent the malicious clients from poisoning

the learning process, and further provides a self-motivated and

reliable learning environment for clients. In detail, the model

aggregation process is fully decentralized and the tasks of training

for FL and mining for blockchain are integrated into each

participant. In addition, we investigate the unique issues in this

framework and provide analytical and experimental results to

shed light on possible solutions.

Index Terms—Federated Learning, Blockchain, Privacy and

Security

I. INTRODUCTION

Future wireless networks are featured by low latency and

high reliability. Thus, machine learning (ML) embedded in

each device is a ravishing solution that each user equipment

(UE) has the capability to make decisions by its local data,

even when it loses connectivity to the wireless system. Since

the data at each device is limited, the training of on-device ML

models always requires the data exchange among UEs [1].

However, directly exchanging data among UEs may cause

serious risks in privacy leakage and information hijacking

[2]. To reduce this risk, federated learning (FL) is proposed,

which is a new ML framework that trains an AI model across

multiple UEs holding local datasets. In details, FL allows to

train machine learning models locally at distributed UEs; after

that, the UEs share the parameters of the locally trained models

to a central server (i.e., the aggregator) where a global model

is aggregated. Therefore, the UEs under the FL framework

C. Ma, J. Li and L. Shi are with the School of Electronic and Optical

Engineering, Nanjing University of Science and Technology, Nanjing, China

(e-mail: {chuan.ma, jun.li}@njust.edu.cn and slong1007@gmail.com).

M. Ding is with Data61, CSIRO, 2015, Australia (e-mail:

Ming.Ding@data61.csiro.au).

T. Wang is with the College of Electronics and Information Engineering,

Shenzhen University, Shenzhen 518060, China (e-mail: ttwang@szu.edu.cn).

Z. Han is with the Department of Electrical and Computer Engineering,

University of Houston, Houston, TX 77004, USA (e-mail: zhan2@uh.edu)

H. V. Poor is with the Department of Electrical Engineering, Princeton

University, Princeton, NJ 08544, USA (e-mail: poor@princeton.edu).

have the capability to cooperatively learn a global model

without exchanging their data directly. Moreover, FL has been

applied to real-world applications, including health care and

autonomous driving [3].

Although FL shows its effectiveness in preserving privacy,

it still endures several limitations. First, in the FL process, the

single centralized aggregator is assumed to be trustworthy and

it shall make fair decisions in terms of the user selection and

aggregation. However, this assumption is not always appropri-

ate, especially in the real-world operations. This is because a

biased aggregator can intentionally emerge prejudice to a few

selected UEs, thereby damaging the learning performance [1].

Second, the aim of FL is restricted to applications orchestrated

by the centralized aggregator. As a result, the resiliency of an

aggregator depends on the robustness of the central server,

and a failure in the aggregator could collapse the entire FL

network. Then, although local data is not explicitly shared

in the original format, it is still possible for adversaries

to reconstruct the raw data approximately, especially in the

aggregation process. In particular, privacy leakage may happen

during model aggregating by outsider attacks. Lastly, the

existing design is vulnerable to the malicious clients that might

upload poisonous models to attack the FL network [4].

As a secure technology, blockchain has the capability to

tolerate single point failure with distributed consensus, and

it can further implement incentive mechanisms to encourage

participants to effectively contribute to the system [5]. There-

fore, blockchain is introduced to FL to solve its limitations

mentioned above. In [5], a blockchained FL architecture was

developed to verify the uploaded parameters and it investigated

the related system performances, such as the learning delay

and the block generation rate. Moreover, work [6] proposed

a privacy-aware architecture that uses blockchain to enhance

security when sharing parameters of machine learning models

with other UEs. In addition, the authors in [7] proposed a

high-level but complicated framework by enabling encryption

during model transmission and providing incentives from

participants, and the work [8] further applied this framework in

the defensive military network. With the advanced features of

blockchain such as tamper-proof, anonymity and traceability,

an immutable audit trail of ML models can be created for

greater trustworthiness in tracking and proving provenance [9].

In addition, security and privacy issues of the decentralized FL

framework are investigated in [6], [10], [11], which delegate

the responsibility of storing ML models to a trust community

in the blockchain. However, the assumption on the trust

arXiv:2009.09338v2 [cs.NI] 4 Jun 2021

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